Correlation between miRseq expression and clinical features
Adrenocortical Carcinoma (Primary solid tumor)
02 April 2015 | analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1PV6JB8
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between miRseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1PV6JB8
Overview
Introduction

This pipeline uses various statistical tests to identify miRs whose log2 expression levels correlated to selected clinical features.

Summary

Testing the association between 518 miRs and 7 clinical features across 80 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 6 clinical features related to at least one miRs.

- 30 miRs correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

- HSA-MIR-130B , HSA-MIR-29C , HSA-MIR-106B , HSA-MIR-664 , HSA-MIR-3170 , ...

- 26 miRs correlated to 'YEARS_TO_BIRTH'.

- HSA-MIR-505 , HSA-MIR-580 , HSA-MIR-361 , HSA-MIR-504 , HSA-MIR-466 , ...

- 30 miRs correlated to 'NEOPLASM_DISEASESTAGE'.

- HSA-MIR-130B , HSA-MIR-196B , HSA-MIR-615 , HSA-MIR-150 , HSA-MIR-155 , ...

- 30 miRs correlated to 'PATHOLOGY_T_STAGE'.

- HSA-MIR-615 , HSA-MIR-489 , HSA-MIR-130B , HSA-MIR-130A , HSA-MIR-196A-1 , ...

- 1 miR correlated to 'PATHOLOGY_N_STAGE'.

- HSA-MIR-510

- 2 miRs correlated to 'GENDER'.

- HSA-MIR-139 , HSA-MIR-3074

- No miRs correlated to 'ETHNICITY'

Results
Overview of the results

Complete statistical result table is provided in Supplement Table 1

Table 1. Get Full Table This table shows the clinical features, statistical methods used, and the number of miRs that are significantly associated with each clinical feature at P value < 0.05 and Q value < 0.3.

Clinical feature | Statistical test | Significant miRs | Associated with |  | Associated with |
--- | --- | --- | --- | --- | --- | ---
DAYS_TO_DEATH_OR_LAST_FUP | Cox regression test | N=30 | shorter survival | N=18 | longer survival | N=12
YEARS_TO_BIRTH | Spearman correlation test | N=26 | older | N=23 | younger | N=3
NEOPLASM_DISEASESTAGE | Kruskal-Wallis test | N=30 |  |  |  |
PATHOLOGY_T_STAGE | Spearman correlation test | N=30 | higher stage | N=24 | lower stage | N=6
PATHOLOGY_N_STAGE | Wilcoxon test | N=1 | n1 | N=1 | n0 | N=0
GENDER | Wilcoxon test | N=2 | male | N=2 | female | N=0
ETHNICITY | Wilcoxon test | N=0 |  |  |  |

Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

30 miRs related to 'DAYS_TO_DEATH_OR_LAST_FUP'.

Table S1. Basic characteristics of clinical feature: 'DAYS_TO_DEATH_OR_LAST_FUP'

DAYS_TO_DEATH_OR_LAST_FUP | Duration (Months) | 4.1-153.6 (median=36.3)
| censored | N = 52
| death | N = 27
| Significant markers | N = 30
| associated with shorter survival | 18
| associated with longer survival | 12

List of top 10 miRs differentially expressed by 'DAYS_TO_DEATH_OR_LAST_FUP'

Table S2. Get Full Table List of top 10 miRs significantly associated with 'Time to Death' by Cox regression test

| HazardRatio | Wald_P | Q | C_index
--- | --- | --- | --- | ---
HSA-MIR-130B | 2.3 | 2.032e-07 | 4.7e-05 | 0.811
HSA-MIR-29C | 0.58 | 2.038e-07 | 4.7e-05 | 0.191
HSA-MIR-106B | 3.4 | 2.705e-07 | 4.7e-05 | 0.787
HSA-MIR-664 | 0.4 | 3.629e-06 | 0.00047 | 0.228
HSA-MIR-3170 | 1.97 | 9.133e-06 | 0.00095 | 0.779
HSA-MIR-937 | 1.56 | 2.129e-05 | 0.0013 | 0.792
HSA-MIR-3940 | 1.93 | 2.232e-05 | 0.0013 | 0.793
HSA-MIR-301B | 1.7 | 2.244e-05 | 0.0013 | 0.742
HSA-MIR-1296 | 2.1 | 2.404e-05 | 0.0013 | 0.716
HSA-MIR-1307 | 2.8 | 2.557e-05 | 0.0013 | 0.75

Clinical variable #2: 'YEARS_TO_BIRTH'

26 miRs related to 'YEARS_TO_BIRTH'.

Table S3. Basic characteristics of clinical feature: 'YEARS_TO_BIRTH'

YEARS_TO_BIRTH | Mean (SD) | 46.4 (16)
| Significant markers | N = 26
| pos. correlated | 23
| neg. correlated | 3

List of top 10 miRs differentially expressed by 'YEARS_TO_BIRTH'

Table S4. Get Full Table List of top 10 miRs significantly correlated to 'YEARS_TO_BIRTH' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
HSA-MIR-505 | 0.4483 | 3.042e-05 | 0.0151
HSA-MIR-580 | 0.4807 | 5.821e-05 | 0.0151
HSA-MIR-361 | 0.4242 | 8.809e-05 | 0.0152
HSA-MIR-504 | 0.452 | 0.0006016 | 0.0779
HSA-MIR-466 | 0.4034 | 0.001266 | 0.131
HSA-MIR-339 | 0.3344 | 0.002431 | 0.21
HSA-MIR-589 | 0.3264 | 0.003132 | 0.217
HSA-MIR-502 | 0.3212 | 0.00367 | 0.217
HSA-MIR-185 | 0.3192 | 0.003902 | 0.217
HSA-MIR-627 | 0.3355 | 0.004239 | 0.217

Clinical variable #3: 'NEOPLASM_DISEASESTAGE'

30 miRs related to 'NEOPLASM_DISEASESTAGE'.

Table S5. Basic characteristics of clinical feature: 'NEOPLASM_DISEASESTAGE'

NEOPLASM_DISEASESTAGE | Labels | N
| STAGE I | 9
| STAGE II | 37
| STAGE III | 16
| STAGE IV | 16
| Significant markers | N = 30

List of top 10 miRs differentially expressed by 'NEOPLASM_DISEASESTAGE'

Table S6. Get Full Table List of top 10 miRs differentially expressed by 'NEOPLASM_DISEASESTAGE'

| kruskal_wallis_P | Q
--- | --- | ---
HSA-MIR-130B | 0.0002726 | 0.078
HSA-MIR-196B | 0.0005301 | 0.078
HSA-MIR-615 | 0.0005365 | 0.078
HSA-MIR-150 | 0.0006681 | 0.078
HSA-MIR-155 | 0.0007663 | 0.078
HSA-MIR-1538 | 0.00103 | 0.078
HSA-MIR-15B | 0.001178 | 0.078
HSA-MIR-130A | 0.00122 | 0.078
HSA-MIR-489 | 0.001355 | 0.078
HSA-MIR-513A-2 | 0.002246 | 0.108

Clinical variable #4: 'PATHOLOGY_T_STAGE'

30 miRs related to 'PATHOLOGY_T_STAGE'.

Table S7. Basic characteristics of clinical feature: 'PATHOLOGY_T_STAGE'

PATHOLOGY_T_STAGE | Mean (SD) | 2.46 (0.98)
|  | N
| T1 | 9
| T2 | 42
| T3 | 9
| T4 | 18
| Significant markers | N = 30
| pos. correlated | 24
| neg. correlated | 6

List of top 10 miRs differentially expressed by 'PATHOLOGY_T_STAGE'

Table S8. Get Full Table List of top 10 miRs significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
HSA-MIR-615 | 0.5097 | 3.507e-06 | 0.00182
HSA-MIR-489 | -0.6649 | 9.729e-06 | 0.00252
HSA-MIR-130B | 0.4483 | 3.855e-05 | 0.00644
HSA-MIR-130A | 0.4425 | 4.972e-05 | 0.00644
HSA-MIR-196A-1 | 0.4288 | 8.988e-05 | 0.00931
HSA-MIR-196A-2 | 0.4206 | 0.0001716 | 0.0148
HSA-MIR-550A-2 | 0.4154 | 0.0002575 | 0.0191
HSA-MIR-16-2 | 0.3955 | 0.0003392 | 0.022
HSA-MIR-196B | 0.3882 | 0.0004455 | 0.0238
HSA-MIR-29C | -0.3873 | 0.0004594 | 0.0238

Clinical variable #5: 'PATHOLOGY_N_STAGE'

One miR related to 'PATHOLOGY_N_STAGE'.

Table S9. Basic characteristics of clinical feature: 'PATHOLOGY_N_STAGE'

PATHOLOGY_N_STAGE | Labels | N
| N0 | 68
| N1 | 10
| Significant markers | N = 1
| Higher in N1 | 1
| Higher in N0 | 0

List of one miR differentially expressed by 'PATHOLOGY_N_STAGE'

Table S10. Get Full Table List of one miR differentially expressed by 'PATHOLOGY_N_STAGE'

| W(pos if higher in 'N1') | wilcoxontestP | Q | AUC
--- | --- | --- | --- | ---
HSA-MIR-510 | 88 | 0.0004316 | 0.224 | 0.8508

Clinical variable #6: 'GENDER'

2 miRs related to 'GENDER'.

Table S11. Basic characteristics of clinical feature: 'GENDER'

GENDER | Labels | N
| FEMALE | 49
| MALE | 31
| Significant markers | N = 2
| Higher in MALE | 2
| Higher in FEMALE | 0

List of 2 miRs differentially expressed by 'GENDER'

Table S12. Get Full Table List of 2 miRs differentially expressed by 'GENDER'. 0 significant gene(s) located in sex chromosomes is(are) filtered out.

| W(pos if higher in 'MALE') | wilcoxontestP | Q | AUC
--- | --- | --- | --- | ---
HSA-MIR-139 | 356 | 6.894e-05 | 0.0357 | 0.7656
HSA-MIR-3074 | 1133 | 0.0002299 | 0.0595 | 0.7459

Clinical variable #7: 'ETHNICITY'

No miR related to 'ETHNICITY'.

Table S13. Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY | Labels | N
| HISPANIC OR LATINO | 8
| NOT HISPANIC OR LATINO | 30
| Significant markers | N = 0

Methods & Data
Input

- Expresson data file = ACC-TP.miRseq_RPKM_log2.txt

- Clinical data file = ACC-TP.merged_data.txt

- Number of patients = 80

- Number of miRs = 518

- Number of clinical features = 7

Selected clinical features

- For clinical features selected for this analysis and their value conozzle.versions, please find a documentation on selected CDEs .

- Survival time data

- Survival time data is a combined value of days_to_death and days_to_last_followup. For each patient, it creates a combined value 'days_to_death_or_last_fup' using conversion process below.

- if 'vital_status'==1(dead), 'days_to_last_followup' is always NA. Thus, uses 'days_to_death' value for 'days_to_death_or_fup'

- if 'vital_status'==0(alive),

- if 'days_to_death'==NA & 'days_to_last_followup'!=NA, uses 'days_to_last_followup' value for 'days_to_death_or_fup'

- if 'days_to_death'!=NA, excludes this case in survival analysis and report the case.

- if 'vital_status'==NA,excludes this case in survival analysis and report the case.

- cf. In certain diesase types such as SKCM, days_to_death parameter is replaced with time_from_specimen_dx or time_from_specimen_procurement_to_death .

- This analysis excluded clinical variables that has only NA values.

Survival analysis

For survival clinical features, Wald's test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values using the 'coxph' function in R. Kaplan-Meier survival curves were plot using the four quartile subgroups of patients based on expression levels

Correlation analysis

For continuous numerical clinical features, Spearman's rank correlation coefficients (Spearman 1904) and two-tailed P values were estimated using 'cor.test' function in R

Wilcoxon rank sum test (Mann-Whitney U test)

For two groups (mutant or wild-type) of continuous type of clinical data, wilcoxon rank sum test (Mann and Whitney, 1947) was applied to compare their mean difference using 'wilcox.test(continuous.clinical ~ as.factor(group), exact=FALSE)' function in R. This test is equivalent to the Mann-Whitney test.

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

References
[1] Andersen and Gill, Cox's regression model for counting processes, a large sample study, Annals of Statistics 10(4):1100-1120 (1982)
[2] Spearman, C, The proof and measurement of association between two things, Amer. J. Psychol 15:72-101 (1904)
[3] Mann and Whitney, On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other, Annals of Mathematical Statistics 18 (1), 50-60 (1947)
[4] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)
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